back_office_ops · saas · workflow
Tiger Data builds a production AI Slack bot with Pydantic AI and Logfire
Tiger Data's entire company operates on Slack, but after reaching a certain size it became nearly impossible for employees to catch up on ongoing conversations, leaving engineers drowning in channel history with no way to surface relevant context instantly.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Slack mention triggers agent
A Slack mention event arrives and kicks off the agent workflow.
Tools used
Pydantic AILogfireSlackOpenTelemetryPostgreSQLpsycopghttpxClaude CodeJinja2JaegerClaude 4.5 SonnetGPT-4oMCP
Outcome
Tiger Data built a production AI Slack bot handling thousands of concurrent conversations, with more than half of the company using it daily within 6 weeks, reduced debugging time, and significant development time savings from LLM provider abstraction.
What failed first
Tiger Data initially built without distributed tracing, which made it very difficult to debug agentic systems in production. They also tried Jaeger for tracing first but found the developer experience poor.
Results
Time savedmore than half of the company uses it daily
Grounding & classification
Source type: technical build writeup
42 fields verified against source quotes.
agentic workflowai agentknowledge searchragchat transcriptknowledge basemeeting recordingsupport ticketbuilder submittedfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionemployee productivitytime savedtechnical build writeupback office opscustomer supportagentic task executionrag answering